Implementasi Perbandingan Metode Knn Dan Naive Bayes Dalam Prediksi Tingkat Kegagalan Mahasiswa Teknik Informatika

 (*)Sahat Roni Mail (Universitas Budi Darma, Medan, Indonesia)
 Fadlina Fadlina (Universitas Budi Darma, Medan, Indonesia)
 Siti Nurhabibah Hutagalung (Universitas Budi Darma Medan, —)

(*) Corresponding Author

Abstract

A tertiary institution certainly wants to have students who are successful in their education but there are very many student failures which have quite a bad impact on the image of the name of the Budidarma University campus, students who fail certainly make many parties feel concerned and other impacts occur in the accumulation of data on students who fail and can reduce the existence of a university. It is necessary to predict the failure rate of students at Budi Darma University to enable the campus to carry out strategies and anticipate even greater student failures. Data mining is a branch of computer science that can help predict the tendency of student data to cause failure rates to make it easier to design and anticipate students so that failure rates in the future do not increase. With the use of predictions using a computer in the form of the RapidMiner application with the K-Nearest Neighbor and Naïve Bayes algorithms, it can make it easier to speed up the process of solving problems correctly.

Keywords


Data Mining;Student Failure Rate;KNN;Naïve Bayes

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Copyright (c) 2022 Sahat Roni, Fadlina Fadlina, Siti Nurhabibah Hutagalung

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KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)
P3M STMIK Budi Darma
Sekretariat Jln. Sisingamangaraja No. 338 Telp 061-7875998
email: komik@univ-bd.ac.id, komik.budidarma@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.